Living Face Verification via Multi-CNNs

DOI

10.2991/ijcis.2018.125905637How to use a DOI?

Keywords

Identity verification; Face recognition; CNN; Bayes probability

Abstract

In face verification applications, precision rate and identifying liveness are two key factors. Traditional methods usually recognize global faces and can not gain good enough results when the faces are captured from different ages, or there are some interference factors, such as facial shade, etc. Besides, the false face attack will pose a great security risk. To solve the above problems, this study examines how to achieve reliable living face verification based on Multi-CNNs (convolutional neural networks) and Bayes probability. Participants are required to make several expressions in random orders and contents, to ensure their liveness. Then, an effective component-based method is proposed for face recognition, and synthesized multiple CNNs can help reflecting intensities of different components. Eventually, the similarity between faces is calculated by integrating the results of each CNN, with the help of Bayes probability. Comparative experiments demonstrate that our algorithm outperforms traditional methods in face recognition accuracy. Moreover, our algorithm has unique preponderance in that it can verify the liveness of users, which can achieve higher security.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Peiqin Li
AU  - Jianbin Xie
AU  - Wei Yan
AU  - Zhen Li
AU  - Gangyao Kuang
PY  - 2018
DA  - 2018/11/01
TI  - Living Face Verification via Multi-CNNs
JO  - International Journal of Computational Intelligence Systems
SP  - 183
EP  - 189
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.125905637
DO  - 10.2991/ijcis.2018.125905637
ID  - Li2018
ER  -